06_post_topic_analysis

pronoun_use_post <- pronoun_use_by_donald_posts %>%
  mutate(id = str_sub(link_id,start = -6)) %>%
  left_join(filter(posts_in_donald, num_comments > 9),by = c("id" = "id")) %>%
  transmute(
            num_comments=num_comments.x,
            collective=collective,
            individual=individual,
            contains_we = contains_we,
            title=title,
            selftext=selftext,
            domain=domain,
            selfpost=ifelse(is.na(selftext),"Not Self Post", "Self Post"),
            score=score,
            created_utc = created_utc,
            date = as.POSIXct(created_utc,tz="", origin="1970-01-01")
            ) %>% filter(#!is.na(collective),
                         num_comments > 500)
colSums(is.na(pronoun_use_post))

num_comments   collective   individual  contains_we        title     selftext       domain 
           0            0            0            0            0         3616            0 
    selfpost        score  created_utc         date 
           0            0            0            0 

Most and least collective posts

pronoun_use_post %>%
  filter(num_comments > 500) %>%
  arrange((collective)) #%>% head(100)

pronoun_use_post %>%
  filter(num_comments > 100) %>%
  ggplot()+
  geom_density(aes(collective))

POT8

Particular words in the title of the post

pronoun_use_post %>%
  filter(num_comments > 100) %>%
  mutate(contains_word = 
        ifelse(
    grepl("TD", (title)) | 
      grepl("t_d", tolower(title)) | 
      grepl("the_donald", tolower(title)) 
      #grepl("censor", tolower(title)) |
      #grepl("environment", tolower(title)) |
      #grepl("ice", tolower(title)) |
      #grepl("", tolower(title)) 
      ,"climate related", "unrelated"
  
                                
                                )) %>%
mutate(self_post = ifelse(is.na(selftext),"no","yes")) %>%
  group_by(contains_word) %>%
  summarise(
    collective_weighted = sum(num_comments*collective)/sum(num_comments),
    collective =  mean(collective),
    num_posts  = n(),
    total_comments = sum(num_comments)
  )

  #ggplot()+
  #geom_boxplot(aes(contains_word,collective))

Time series analysis

library(plotly)
ggplotly(
  ggplot(filter(pronoun_use_post,num_comments >150) ,aes(date,collective,size = num_comments, name = title, color = selfpost))+
  geom_point()
)

ALL POLITICAL SUBREDDITS

pronoun_use_post <- pronoun_use_by_political_posts %>%
  mutate(id = str_sub(link_id,start = -6)) %>%
  left_join(filter(posts_in_political_subs, num_comments > 9),by = c("id" = "id", "subreddit" = "subreddit") )%>%
  transmute(subreddit = subreddit,
            num_comments=num_comments.x,
            collective=collective,
            individual=individual,
            col_min_ind = contains_we - individual,
            contains_we = contains_we,
            title=title,
            full_title = paste(subreddit,title, sep=": "),
            selftext=selftext,
            domain=domain,
            selfpost=ifelse(is.na(selftext),"Not Self Post", "Self Post"),
            score=score,
            created_utc = created_utc,
            date = as.POSIXct(created_utc,tz="", origin="1970-01-01"),
            climate_change = ifelse(
    grepl("climate", tolower(title)) | 
      grepl("temperature", tolower(title)) | 
      #grepl("pollution", tolower(title)) | 
      grepl("warming", tolower(title)) |
      grepl("environment", tolower(title)) |
      #grepl("ice", tolower(title)) |
      grepl("environment", tolower(title)) 
      ,"climate related", "unrelated"
  ),
  threat = ifelse(
    grepl("threat", tolower(title)) | 
      grepl("extinct", tolower(title)) | 
      grepl("warns", tolower(title)) 
      ,"threat related", "unrelated"
  ),
  aliens = ifelse(
    grepl("alien", tolower(title))
      
      ,"alien related", "unrelated"
  ),
  any_of_three = ifelse(
     climate_change == "climate related"| 
      threat == "threat related" |
      aliens == "alien related"
      ,"any of 3 related", "unrelated"
  )
            ) %>% filter(#!is.na(collective),
                         num_comments > 50) %>%
  left_join(read_csv("subreddit_categories.csv"), by = c("subreddit" = "subreddit"))

Parsed with column specification:
cols(
  subreddit = col_character(),
  topic_level1 = col_character(),
  topic_level2 = col_character(),
  topic_level3 = col_character(),
  t1_t2 = col_character()
)

worldnews

ALL POLITICAL SUBREDDITS

pronoun_use_post <- pronoun_use_by_worldnews2016_posts %>%
  mutate(id = str_sub(link_id,start = -6)) %>%
  left_join(filter(posts_in_political_subs, subreddit == "worldnews"),by = c("id" = "id" ))%>%
  transmute(subreddit = subreddit,
            num_comments=num_comments.x,
            collective = collective,
            score = score.x,
            
            
           
            title=title,
            
            selftext=selftext,
            domain=domain,
            selfpost=ifelse(is.na(selftext),"Not Self Post", "Self Post"),
            
            created_utc = created_utc,
            date = as.POSIXct(created_utc,tz="", origin="1970-01-01")) %>% 
            filter(#!is.na(collective),
                         num_comments > 50) %>%
  left_join(read_csv("subreddit_categories.csv"), by = c("subreddit" = "subreddit"))

Parsed with column specification:
cols(
  subreddit = col_character(),
  topic_level1 = col_character(),
  topic_level2 = col_character(),
  topic_level3 = col_character(),
  t1_t2 = col_character()
)

colSums(is.na(pronoun_use_post))

   subreddit num_comments   collective        score        title     selftext       domain 
           1            0            0            0            1         4658            1 
    selfpost  created_utc         date topic_level1 topic_level2 topic_level3        t1_t2 
           0            1            1            1            1         4841            1 

reg worldnews

worldnews_posts <- read_delim("worldnews_environmental_posts - worldnews_posts.tsv", 
    "\t", escape_double = FALSE, trim_ws = TRUE) %>%
  mutate(threat_score = case_when(
    threat_score == "e" ~ "Environmental Related",
    threat_score == "a" ~ "Alien/Space travel Related",
    TRUE ~ "Other"
    
  ))  %>% filter(!is.na(title))

Parsed with column specification:
cols(
  .default = col_character(),
  num_comments = col_integer(),
  collective = col_double(),
  individual = col_double(),
  col_min_ind = col_double(),
  contains_we = col_double(),
  score = col_integer(),
  created_utc = col_integer(),
  date = col_datetime(format = "")
)
See spec(...) for full column specifications.
number of columns of result is not a multiple of vector length (arg 1)8 parsing failures.
row # A tibble: 5 x 5 col     row col         expected     actual     file                                                  expected   <int> <chr>       <chr>        <chr>      <chr>                                                 actual 1  1119 score       an integer   unrelated  'worldnews_environmental_posts - worldnews_posts.tsv' file 2  1119 created_utc an integer   unrelated  'worldnews_environmental_posts - worldnews_posts.tsv' row 3  1119 date        "date like " unrelated  'worldnews_environmental_posts - worldnews_posts.tsv' col 4  1119 NA          24 columns   21 columns 'worldnews_environmental_posts - worldnews_posts.tsv' expected 5  2404 score       an integer   unrelated  'worldnews_environmental_posts - worldnews_posts.tsv'
... ................................. ... ................................................................................................. ........ .............................................................................................................................................................................................................................. ...... .......................................................................................................................... .... .......................................................................................................................... ... .......................................................................................................................................................... ... .................................................................................................................................... ........ ..........................................................................................................................
See problems(...) for more details.

POT 7

library(plotly)
#ggplotly(
  ggplot(worldnews_posts ,aes(date,collective,size = num_comments, name = full_title, color = threat_score))+
  geom_point()+
    theme_classic()+
    scale_y_continuous()

                  
#)
worldnews_posts %>% group_by(threat_score) %>% summarise(
  number_of_posts = n(),
  avg_collective = mean(collective),
  avg_individual = mean(individual)
)

ggplot(mutate(worldnews_posts,col_pct = 100*collective) ,aes(threat_score,col_pct,size = num_comments))+ geom_boxplot() + geom_jitter(alpha = 0.1) + theme_classic(base_size = 16)+
       labs(title = "Collective Pronoun Use by Topic in posts from /r/worldnews",
       subtitle = "All posts from 2016 with at least 500 comments, each circle represents a post scaled by number of comments",
       caption = "") + xlab("")+ ylab("Percentage of Comments which contains Collective Pronouns") 

       
ggplot(worldnews_posts ,aes(threat_score,individual,size = num_comments))+ geom_boxplot() 

#worldnews_posts %>% filter(threat_score == "Environmental Related") %>% arrange(desc(collective)) %>% select(collective,title, num_comments, individual,contains_we,date) %>% write_csv("worldnews_environment2.csv")

Hyp test

t.test(oss$collective,glob$collective, alternative =  "greater", mu = 0, 
    paired = FALSE, var.equal = FALSE, conf.level = 0.95)

    Welch Two Sample t-test

data:  oss$collective and glob$collective
t = 1.9619, df = 7.8336, p-value = 0.04309
alternative hypothesis: true difference in means is greater than 0
95 percent confidence interval:
 0.002858331         Inf
sample estimates:
mean of x mean of y 
0.3409869 0.2833103 

Score analysis by threat scale

POT99

  
  
  worldnews_graphs <- worldnews_environment_threat_level %>% 
  #filter(problem == "problem") %>% 
  select(title, threat_level, problem) %>%
  left_join(worldnews_posts,., by = c("title" = "title")) %>% 
  select(-(score)) %>%
  mutate(threat_level = case_when(
    problem == "Resolution" ~ "All Environmental Resolutions",
    !is.na(threat_level) ~ as.character(threat_level),
     star_system == "o" ~  "Outside Solar System",
    star_system == "i" ~  "Inside Solar System",
               TRUE~"All Other"
                                  ), 
         threat_level = factor(threat_level,threat_levels2)) %>% left_join(pronoun_use_post,., by = c("title" = "title")) %>% filter(!is.na(threat_level))





worldnews_graphs2 <- worldnews_graphs %>% group_by(title) %>% summarise(score_avg = mean(score)) %>% ungroup() %>% 
 left_join(worldnews_graphs,., by = c("title" = "title")) %>% mutate(score_ratio =
             score/(score -2*(score-score_avg))                         
              ) %>%
  filter(collective.x == "Contains collective")

    ggplot(worldnews_graphs2,
       aes(threat_level,score_ratio, size = num_comments.x))+ geom_boxplot(outlier.alpha = 0) + 
  geom_jitter(width=.25,alpha = 0.2)  +
  theme_classic() +  labs(title = "Score Ratio between comments with Collective Pronouns and Without in Posts from /r/worldnews by Scale of Threat/Resolution",
       subtitle = "all posts from 2016 with at least 500 comments, each circle represents a post scaled by number of comments",
       caption = "") + geom_hline(yintercept = 1) + theme_classic(base_size = 12) +xlab("") + ylab("Score ratio between comments with and without collective pronouns for each post") +scale_y_continuous(limits = c(0,4)) + theme(axis.text=element_text(size=8))
  
## hypothesis testing
    worldnews_graphs2 %>%
  group_by(problem,threat_level) %>%
  summarise(
    total_comments = sum(num_comments.x),
    collective_avg = sum(collective*num_comments)/total_comments,
    individual_avg = sum(individual*num_comments)/total_comments,
    col_min =collective_avg + sqrt(collective_avg*(1-collective_avg)/total_comments),
    col_max =collective_avg - sqrt(collective_avg*(1-collective_avg)/total_comments)
  )
  
  
  #only environmental
  
  worldnews_graphs %>% group_by(title) %>% summarise(score_avg = mean(score)) %>% ungroup() %>% 
 left_join(worldnews_graphs,., by = c("title" = "title")) %>% mutate(score_ratio = score/score_avg) %>%
    filter(climate_change == "related") %>%
    ggplot(
       aes(threat_level,score_ratio, size = num_comments.x))+ geom_boxplot(outlier.alpha = 0) + 
  geom_jitter(width=.25,alpha = 0.2) + facet_grid(~collective.x) +
  theme_classic() + labs(title = "Score Ratio between comments with Collective Pronouns and Without in Posts from /r/worldnews by Scale of Threat/Resolution",
       subtitle = "all posts from 2016 with at least 500 comments, each circle represents a post scaled by number of comments",
       caption = "") + geom_hline(yintercept = 1) + theme_classic() +xlab("") + ylab("Average score ratio between comments with and without collective pronouns") 
  

HYPOTHESIS TESTING